Open Access Research article

A second generation framework for the analysis of microsatellites in expressed sequence tags and the development of EST-SSR markers for a conifer, Cryptomeria japonica

Saneyoshi Ueno1*, Yoshinari Moriguchi1, Kentaro Uchiyama1, Tokuko Ujino-Ihara1, Norihiro Futamura2, Tetsuya Sakurai3, Kenji Shinohara2 and Yoshihiko Tsumura1

Author affiliations

1 Department of Forest Genetics, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan

2 Department of Molecular and Cell Biology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan

3 Integrated Genome Informatics Research Unit, Plant Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan

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Citation and License

BMC Genomics 2012, 13:136  doi:10.1186/1471-2164-13-136

Published: 16 April 2012

Abstract

Background

Microsatellites or simple sequence repeats (SSRs) in expressed sequence tags (ESTs) are useful resources for genome analysis because of their abundance, functionality and polymorphism. The advent of commercial second generation sequencing machines has lead to new strategies for developing EST-SSR markers, necessitating the development of bioinformatic framework that can keep pace with the increasing quality and quantity of sequence data produced. We describe an open scheme for analyzing ESTs and developing EST-SSR markers from reads collected by Sanger sequencing and pyrosequencing of sugi (Cryptomeria japonica).

Results

We collected 141,097 sequence reads by Sanger sequencing and 1,333,444 by pyrosequencing. After trimming contaminant and low quality sequences, 118,319 Sanger and 1,201,150 pyrosequencing reads were passed to the MIRA assembler, generating 81,284 contigs that were analysed for SSRs. 4,059 SSRs were found in 3,694 (4.54%) contigs, giving an SSR frequency lower than that in seven other plant species with gene indices (5.4–21.9%). The average GC content of the SSR-containing contigs was 41.55%, compared to 40.23% for all contigs. Tri-SSRs were the most common SSRs; the most common motif was AT, which was found in 655 (46.3%) di-SSRs, followed by the AAG motif, found in 342 (25.9%) tri-SSRs. Most (72.8%) tri-SSRs were in coding regions, but 55.6% of the di-SSRs were in non-coding regions; the AT motif was most abundant in 3′ untranslated regions. Gene ontology (GO) annotations showed that six GO terms were significantly overrepresented within SSR-containing contigs. Forty–four EST-SSR markers were developed from 192 primer pairs using two pipelines: read2Marker and the newly-developed CMiB, which combines several open tools. Markers resulting from both pipelines showed no differences in PCR success rate and polymorphisms, but PCR success and polymorphism were significantly affected by the expected PCR product size and number of SSR repeats, respectively. EST-SSR markers exhibited less polymorphism than genomic SSRs.

Conclusions

We have created a new open pipeline for developing EST-SSR markers and applied it in a comprehensive analysis of EST-SSRs and EST-SSR markers in C. japonica. The results will be useful in genomic analyses of conifers and other non-model species.